In the state of the art it is known to classify objects into target classes by means of Doppler-broadened radar echo signals. Neural networks are used for the classification. A device and a method for the automatic classification of objects, which uses a neural network of the multi-layer perceptron type with a layer of input nodes for features of the Doppler-broadened radar echo signals, with hidden layers and a layer of output nodes, is described in an essay by J. Martinez Madrid et al: "A Neural Network Approach to Doppler-Based Target Classification", Universidad Politecnica de Madrid, Spain, pages 450 to 453.
A device for classifying data that change with time, particularly for classifying objects by means of their time-dependent Doppler radar or sonar signatures is described in the international patent application WO 91/02323 (PCT/US90/04487). It is designed for processing two-dimensional data, namely of frequency bands and pertinent time slots. To that end it has a neural network with at least N+1 input nodes, with N as the number of frequency bands, and for classifying the object the value of a time slot and the associated values of the individual frequency bands are presented to the input nodes of the neural network.
A further method for classifying objects is known from an article by M. Menon entitled "An Automatic Ship Classification System for ISAR Imagery", SPIE Volume 2492 1995, pages 373 to 388. In that case ships are divided into classes by means of digitized video images of an ISAR radar device using a neural network of the adaptive clustering network type. Feature vectors of different video images are presented to the neural network.
The devices and methods for classifying objects known in the state of the art have the disadvantage that their reliability of classification assurance depends on environmental influences and, if other environmental influences prevail during the operating phase than during the determination of the training data, they have relatively high rates of wrong classifications.